Despite substantial research into the biological basis of memory, the precise mechanisms by which experiences are encoded, stored, and retrieved in the brain remain incompletely understood. A growing body of evidence supports the engram theory, which posits that sparse populations of neurons undergo lasting physical and biochemical changes to support long-term memory. Yet, a comprehensive computational framework that integrates biological findings with mechanistic models remains elusive. This work synthesizes insights from cellular neuroscience and computational modeling to address key challenges in engram research: how engram neurons are identified and manipulated; how synaptic plasticity mechanisms contribute to stable memory traces; and how sparsity promotes efficient, interference-resistant representations. Relevant computational approaches -- such as sparse regularization, engram gating, and biologically inspired architectures like Sparse Distributed Memory and spiking neural networks -- are also examined. Together, these findings suggest that memory efficiency, capacity, and stability emerge from the interaction of plasticity and sparsity constraints. By integrating neurobiological and computational perspectives, this paper provides a comprehensive theoretical foundation for engram research and proposes a roadmap for future inquiry into the mechanisms underlying memory, with implications for the diagnosis and treatment of memory-related disorders.

翻译：尽管对记忆的生物学基础已有大量研究，但大脑中经验编码、存储和检索的确切机制仍未完全阐明。越来越多的证据支持印迹理论，该理论认为稀疏的神经元群经历持久的物理和生化变化以支持长期记忆。然而，一个整合生物学发现与机制模型的全面计算框架仍然难以实现。本研究综合了细胞神经科学和计算建模的见解，以应对印迹研究中的关键挑战：如何识别和操控印迹神经元；突触可塑性机制如何促进稳定的记忆痕迹；以及稀疏性如何促进高效、抗干扰的表征。文中还探讨了相关的计算方法——如稀疏正则化、印迹门控以及受生物学启发的架构，如稀疏分布式记忆和脉冲神经网络。这些发现共同表明，记忆效率、容量和稳定性源于可塑性与稀疏性约束的相互作用。通过整合神经生物学和计算视角，本文为印迹研究提供了全面的理论基础，并为未来探究记忆的潜在机制提出了路线图，这对记忆相关疾病的诊断和治疗具有启示意义。